library(phyloseq)
library(dplyr)
library(tidyr)
library(tibble)
library(ggplot2)
library(vegan)
library(readr)
library(ampvis)
load(file = "SOB_files.rda")
# Make a data frame with a column for the read counts of each sample
sample_sum_df <- data.frame(sum = sample_sums(SOB_data))
# Histogram of sample read counts
ggplot(sample_sum_df, aes(x = sum)) +
geom_histogram(color = "black", fill = "indianred", binwidth = 2500) +
ggtitle("Distribution of sample sequencing depth") +
xlab("Read counts") +
theme(axis.title.y = element_blank())
#Standardize abundances to the median sequencing depth
total <- median(sample_sums(SOB_data))
standf <- function(x, t=total) round(t * (x/sum(x)))
SOB_data.std <- transform_sample_counts(SOB_data, standf)
#Filter taxa with cutoff 3.0 Coefficient of Variation
SOB_data.stdf <- filter_taxa(SOB_data.std, function(x) sd(x)/mean(x) > 3.0, TRUE)
fungi.p <- subset_taxa(SOB_data.stdf, Phylum!="p__unidentified")
fungi.p <- tax_glom(fungi.p, taxrank="Phylum")
fungi.p2 <- subset_samples(fungi.p, site_code != "null")
code_names <- c(INV = "Invasive",
PL = "Plantation",
UN = "Native")
bar_phyla_code <- plot_bar(fungi.p2, x = "site_name", fill = "Phylum")
bar_phyla_code + geom_bar(stat = "identity", color="black", size=0.2,position = "stack") +
facet_grid(site_code ~ ., labeller = labeller(site_code = code_names)) +
scale_fill_brewer(type = "div", palette = "Paired")
#Transforming to proportions
SOB_data.prop <- transform_sample_counts(SOB_data.stdf, function(otu) otu/sum(otu))
SOB_data.prop1 <- subset_samples(SOB_data.prop, site_code != "null")
#Palette
library(RColorBrewer)
pal <- colorRampPalette(brewer.pal(12, "Paired"))
#Ordination
ord.SOB <- ordinate(SOB_data.prop1, "NMDS", "bray")
## Run 0 stress 0.3272439
## Run 1 stress 0.3358327
## Run 2 stress 0.333735
## Run 3 stress 0.3252148
## ... New best solution
## ... Procrustes: rmse 0.0197577 max resid 0.2294536
## Run 4 stress 0.3251114
## ... New best solution
## ... Procrustes: rmse 0.02524615 max resid 0.2186668
## Run 5 stress 0.327906
## Run 6 stress 0.3246175
## ... New best solution
## ... Procrustes: rmse 0.01966239 max resid 0.1586432
## Run 7 stress 0.4182376
## Run 8 stress 0.325142
## Run 9 stress 0.3259399
## Run 10 stress 0.3229439
## ... New best solution
## ... Procrustes: rmse 0.02228391 max resid 0.2227812
## Run 11 stress 0.3231946
## ... Procrustes: rmse 0.007421908 max resid 0.1053651
## Run 12 stress 0.3233585
## ... Procrustes: rmse 0.01428077 max resid 0.2275235
## Run 13 stress 0.3301648
## Run 14 stress 0.323716
## Run 15 stress 0.3262954
## Run 16 stress 0.3242287
## Run 17 stress 0.324826
## Run 18 stress 0.3245281
## Run 19 stress 0.3255049
## Run 20 stress 0.3239731
## *** No convergence -- monoMDS stopping criteria:
## 3: no. of iterations >= maxit
## 17: stress ratio > sratmax
plot_ordination(SOB_data.prop1, ord.SOB, shape = "site_code", color = "site_name") +
geom_point(size = 3) + scale_color_manual(values = pal(42)) + labs(title = "All samples indicated by site name and code")
#Filtering data
SOB_data.prop.PL <- subset_samples(SOB_data.prop, site_code == "PL")
#Ordination
ord.SOB.PL <- ordinate(SOB_data.prop.PL, "NMDS", "bray")
## Run 0 stress 0.2891296
## Run 1 stress 0.2939355
## Run 2 stress 0.2886745
## ... New best solution
## ... Procrustes: rmse 0.02097771 max resid 0.1585497
## Run 3 stress 0.289357
## Run 4 stress 0.2892885
## Run 5 stress 0.2893581
## Run 6 stress 0.2908114
## Run 7 stress 0.2889711
## ... Procrustes: rmse 0.01903144 max resid 0.1756168
## Run 8 stress 0.291271
## Run 9 stress 0.2905243
## Run 10 stress 0.2923748
## Run 11 stress 0.2893052
## Run 12 stress 0.2924048
## Run 13 stress 0.290536
## Run 14 stress 0.28889
## ... Procrustes: rmse 0.004728383 max resid 0.0489993
## Run 15 stress 0.2893555
## Run 16 stress 0.2896061
## Run 17 stress 0.2908989
## Run 18 stress 0.2878915
## ... New best solution
## ... Procrustes: rmse 0.01305592 max resid 0.119393
## Run 19 stress 0.2910043
## Run 20 stress 0.2877801
## ... New best solution
## ... Procrustes: rmse 0.01008002 max resid 0.1079592
## *** No convergence -- monoMDS stopping criteria:
## 3: no. of iterations >= maxit
## 17: stress ratio > sratmax
plot_ordination(SOB_data.prop.PL, ord.SOB.PL, color = "state") +
geom_point(size = 3) + scale_color_brewer(type = "div", palette = "Set1",
name = "State",
labels = c("Australian Capital Territory",
"New South Wales",
"South Australia",
"Victoria",
"Western Australia")) +
labs(title = "Plantation sites colored by state")
#Filtering data
SOB_data.prop.UN <- subset_samples(SOB_data.prop,
site_code == "UN" |
site_code == "null")
#Ordination
ord.SOB.UN <- ordinate(SOB_data.prop.UN, "NMDS", "bray")
## Run 0 stress 0.295749
## Run 1 stress 0.3032717
## Run 2 stress 0.2927674
## ... New best solution
## ... Procrustes: rmse 0.05524421 max resid 0.229019
## Run 3 stress 0.2887389
## ... New best solution
## ... Procrustes: rmse 0.0456279 max resid 0.2371747
## Run 4 stress 0.2904268
## Run 5 stress 0.2956856
## Run 6 stress 0.2996751
## Run 7 stress 0.2885097
## ... New best solution
## ... Procrustes: rmse 0.02267487 max resid 0.2460066
## Run 8 stress 0.2917857
## Run 9 stress 0.2916008
## Run 10 stress 0.2966288
## Run 11 stress 0.2947973
## Run 12 stress 0.2931249
## Run 13 stress 0.2893764
## Run 14 stress 0.2940425
## Run 15 stress 0.2988873
## Run 16 stress 0.2902169
## Run 17 stress 0.2926751
## Run 18 stress 0.3011673
## Run 19 stress 0.2885058
## ... New best solution
## ... Procrustes: rmse 0.02728989 max resid 0.2492674
## Run 20 stress 0.3004677
## *** No convergence -- monoMDS stopping criteria:
## 2: no. of iterations >= maxit
## 18: stress ratio > sratmax
plot_ordination(SOB_data.prop.UN, ord.SOB.UN, color = "state", shape = "site_code") +
geom_point(size = 3) + scale_color_brewer(type = "div", palette = "Set1",
name = "State",
labels = c("Australian Capital Territory",
"New South Wales",
"South Australia",
"Victoria",
"Western Australia")) +
scale_shape_discrete(labels = c("Nullabor", "Native"), name = "Site") +
labs(title = "Native sites, including Nullabor, colored by state")
#Filtering data
SOB_data.prop.UN1 <- subset_samples(SOB_data.prop, site_code == "UN")
#Ordination
ord.SOB.UN1 <- ordinate(SOB_data.prop.UN1, "NMDS", "bray")
## Run 0 stress 0.3250856
## Run 1 stress 0.3248333
## ... New best solution
## ... Procrustes: rmse 0.03782476 max resid 0.2085237
## Run 2 stress 0.3146883
## ... New best solution
## ... Procrustes: rmse 0.06303572 max resid 0.2198325
## Run 3 stress 0.3166515
## Run 4 stress 0.3253467
## Run 5 stress 0.3143736
## ... New best solution
## ... Procrustes: rmse 0.009989011 max resid 0.08670513
## Run 6 stress 0.3268375
## Run 7 stress 0.3180081
## Run 8 stress 0.3154716
## Run 9 stress 0.3284646
## Run 10 stress 0.3146426
## ... Procrustes: rmse 0.0274896 max resid 0.1444298
## Run 11 stress 0.3310118
## Run 12 stress 0.3156499
## Run 13 stress 0.3146445
## ... Procrustes: rmse 0.009835445 max resid 0.08641049
## Run 14 stress 0.3146157
## ... Procrustes: rmse 0.01239383 max resid 0.08405933
## Run 15 stress 0.321096
## Run 16 stress 0.3156904
## Run 17 stress 0.3318211
## Run 18 stress 0.3153562
## Run 19 stress 0.3247976
## Run 20 stress 0.3196146
## *** No convergence -- monoMDS stopping criteria:
## 2: no. of iterations >= maxit
## 18: stress ratio > sratmax
plot_ordination(SOB_data.prop.UN1, ord.SOB.UN1, color = "state") +
geom_point(size = 3) +
scale_color_brewer(type = "div", palette = "Set1", name = "State",
labels = c("Australian Capital Territory",
"New South Wales",
"South Australia",
"Victoria",
"Western Australia")) +
scale_shape_discrete(labels = c("Nullabor", "Native"), name = "Site") +
labs(title = "Native sites colored by state")
#Filtering data only to plantation and native sites
SOB_data.stdf.1 <- subset_samples(SOB_data.stdf,
site_code == "UN" |
site_code == "PL")
#Heatmap
amp_heatmap(data = SOB_data.stdf.1,
group = c("state", "site_code"),
tax.show = 50,
scale.seq = 100,
plot.text.size = 2,
tax.aggregate = "Genus",
tax.add = "Family")
## Warning: Transformation introduced infinite values in discrete y-axis
Tree1
#Tree visual
set.seed(1)
metacoder::heat_tree_matrix(taxa::filter_taxa(obj, taxon_names == "c__Agaricomycetes", subtaxa = TRUE),
dataset = "diff_table",
node_size = n_obs,
node_label = taxon_names,
node_color = log2_median_ratio,
node_color_range = c("#a6611a","#dfc27d","#bdbdbd","#80cdc1","#018571"),
node_color_trans = "linear",
node_label_max = 120,
node_color_interval = c(-1, 1),
edge_color_interval = c(-1, 1),
node_size_axis_label = "Number of OTUs",
node_color_axis_label = "Log2 ratio median proportions",
initial_layout = "reingold-tilford", layout = "davidson-harel")
#Heatmap
ht.type <- amp_heatmap(data = SOB_data.stdf,
group = "site_code",
tax.show = 100,
scale.seq = 100,
plot.text.size = 2,
tax.aggregate = "Genus",
tax.add = "Order")
ht.type + scale_x_discrete(breaks = c("INV", "null", "PL", "UN"),
labels = c("Invasive", "Nullabor", "Plantation", "Native"))
## Warning: Transformation introduced infinite values in discrete y-axis
Plantation
SOB_data.stdf.PL <- subset_samples(SOB_data.stdf, site_code == "PL")
amp_rabund(SOB_data.stdf.PL,
tax.aggregate = "Genus",
tax.add = "Order",
scale.seq = 10,
tax.show = 80,
adjust.zero = 0.1,
plot.log = TRUE)
## 80
Native
SOB_data.stdf.UN <- subset_samples(SOB_data.stdf, site_code == "UN")
amp_rabund(SOB_data.stdf.UN,
tax.aggregate = "Genus",
tax.add = "Order",
scale.seq = 10,
tax.show = 80,
adjust.zero = 0.1,
plot.log = TRUE)
## 80
Invasive
SOB_data.stdf.INV <- subset_samples(SOB_data.stdf, site_code == "INV")
amp_rabund(SOB_data.stdf.INV,
tax.aggregate = "Genus",
tax.add = "Order",
scale.seq = 10,
tax.show = 80,
adjust.zero = 0.1,
plot.log = TRUE)
## 80